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Variable Rate Point Cloud Geometry Compression Method
With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302694/ https://www.ncbi.nlm.nih.gov/pubmed/37420640 http://dx.doi.org/10.3390/s23125474 |
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author | Zhuang, Lehui Tian, Jin Zhang, Yujin Fang, Zhijun |
author_facet | Zhuang, Lehui Tian, Jin Zhang, Yujin Fang, Zhijun |
author_sort | Zhuang, Lehui |
collection | PubMed |
description | With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performance. However, there is a one-to-one correspondence between the model and the compression rate in these methods. To achieve compression at different rates, a large number of models need to be trained, which increases the training time and storage space. To address this problem, a variable rate point cloud compression method is proposed, which enables the adjustment of the compression rate by the hyperparameter in a single model. To address the narrow rate range problem that occurs when the traditional rate distortion loss is jointly optimized for variable rate models, a rate expansion method based on contrastive learning is proposed to expands the bit rate range of the model. To improve the visualization effect of the reconstructed point cloud, a boundary learning method is introduced to improve the classification ability of the boundary points through boundary optimization and enhance the overall model performance. The experimental results show that the proposed method achieves variable rate compression with a large bit rate range while ensuring the model performance. The proposed method outperforms G-PCC, achieving more than 70% BD-Rate against G-PCC, and performs about, as well as the learned methods at high bit rates. |
format | Online Article Text |
id | pubmed-10302694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103026942023-06-29 Variable Rate Point Cloud Geometry Compression Method Zhuang, Lehui Tian, Jin Zhang, Yujin Fang, Zhijun Sensors (Basel) Article With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performance. However, there is a one-to-one correspondence between the model and the compression rate in these methods. To achieve compression at different rates, a large number of models need to be trained, which increases the training time and storage space. To address this problem, a variable rate point cloud compression method is proposed, which enables the adjustment of the compression rate by the hyperparameter in a single model. To address the narrow rate range problem that occurs when the traditional rate distortion loss is jointly optimized for variable rate models, a rate expansion method based on contrastive learning is proposed to expands the bit rate range of the model. To improve the visualization effect of the reconstructed point cloud, a boundary learning method is introduced to improve the classification ability of the boundary points through boundary optimization and enhance the overall model performance. The experimental results show that the proposed method achieves variable rate compression with a large bit rate range while ensuring the model performance. The proposed method outperforms G-PCC, achieving more than 70% BD-Rate against G-PCC, and performs about, as well as the learned methods at high bit rates. MDPI 2023-06-09 /pmc/articles/PMC10302694/ /pubmed/37420640 http://dx.doi.org/10.3390/s23125474 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhuang, Lehui Tian, Jin Zhang, Yujin Fang, Zhijun Variable Rate Point Cloud Geometry Compression Method |
title | Variable Rate Point Cloud Geometry Compression Method |
title_full | Variable Rate Point Cloud Geometry Compression Method |
title_fullStr | Variable Rate Point Cloud Geometry Compression Method |
title_full_unstemmed | Variable Rate Point Cloud Geometry Compression Method |
title_short | Variable Rate Point Cloud Geometry Compression Method |
title_sort | variable rate point cloud geometry compression method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302694/ https://www.ncbi.nlm.nih.gov/pubmed/37420640 http://dx.doi.org/10.3390/s23125474 |
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